This study investigates multiresponse optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA).
Trang 1* Corresponding author Tel: 09883738503
E-mail: alfa.nita2010@gmail.com (A Saha)
© 2013 Growing Science Ltd All rights reserved
doi: 10.5267/j.ijiec.2012.011.004
International Journal of Industrial Engineering Computations 4 (2013) 51–60
Contents lists available at GrowingScience
International Journal of Industrial Engineering Computations
homepage: www.GrowingScience.com/ijiec
Optimization of machining parameters of turning operations based on multi performance
criteria
a M Tech.Student, National Institute of Technical Teachers Training & Research, Kolkata 700106,India
b Associate Professor, National Institute of Technical Teachers Training & Research, Kolkata, India
C H R O N I C L E A B S T R A C T
Article history:
Received August 20 2012
Received in revised format
November 18 2012
Accepted November 20 2012
Available online
21 November 2012
The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue This study investigates multi-response optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA) Confirmation test is conducted for the optimal machining parameters to validate the test result Various turning parameters, such as spindle speed, feed and depth of cut are considered Experiments are designed and conducted based on full factorial design of experiment
© 2013 Growing Science Ltd All rights reserved
Keywords:
Turning
Power consumption
Surface roughness
Grey relational analysis
Frequency of tool vibration
1 Introduction
Turning is one of the most basic machining processes in industrial production systems Turning process can produce various shapes of materials such as straight, conical, curved, or grooved work pieces In general, turning uses simple single-point cutting tools Many researchers have studied the effects of optimal selection of machining parameters in turning Tzeng and Chen (2006) used grey relational analysis to optimize the process parameters in turning of tool steels They performed Taguchi experiments with eight independent variables including cutting speed, feed, and depth of cut, coating type, type of insert, chip breaker geometry, coolant, and band nose radius The optimum turning parameters were determined based on grey relational grade, which maximizes the accuracy and minimizes the surface roughness and dimensional precision
Similarly, the researchers have applied grey relational analysis (GRA) to different machining processes, which include electric discharge machining Lin et al (2002), determining tool condition in turning (Lo, 2002), chemical mechanical polishing (Lin & Ho, 2003), side milling (Chang & Lu, 2007),
Trang 252
and flank milling (Kopac & Krajnik, 2007) to compare the performance of diamond tool carbide inserts
in dry turning (Arumugam et al., 2006), and optimization of drilling parameters to minimize surface roughness and burr height (Tosun, 2006) Lin (2004) implemented grey relational analysis to optimize turning operations with multiple performance characteristics He analyzed tool life, cutting force, and surface roughness in turning operations
Tosun (2006) reported the use of grey relational analysis for optimizing the drilling process parameters for the work piece surface roughness and the burr height is introduced This study indicated that grey relational analysis approach can be applied successfully to other operations in which performance is determined by many parameters at multiple quality requests Al-Refaie et al (2010) used Taguchi method grey analysis (TMGA) to determine the optimal combination of control parameters in milling, the measures of machining performance being the MRR and SR
Based on the ANOVA; it was found that the feed rate is important control factor for both machining responses If there are multiple response variables for the same set of independent variables, the methodology provides a different set of optimum operating conditions for each response variable The grey system theory initiated by Deng (1982) has been proven to be useful for dealing with poor, incomplete, and uncertain information The grey relational based on the grey system theory can be used
to solve the complicated interrelationships among the multiple performance characteristics effectively (Wang et al., 1996)
Therefore, the purpose of the present work is to introduce the use of grey relational analysis in selecting optimum turning conditions on multi-performance characteristics, namely the surface roughness, power consumption and frequency of tool vibration In addition, the most effective factor and the order of importance of the controllable factors to the multi-performance characteristics in the turning process were determined
The cutting experiments were carried out on an experimental lathe setup using a HSS MIRANDA
S-400 (AISI T – 42) cutting tool for the machining of the IS: 2062, Gr B Mild Steel bar, which is 24 mm
in diameter The percent composition of the work piece material is listed in Table 1 Mar Surf PS1 surface roughness tester was used to measure the Surface roughness Ra (µm) of the machined samples and Lathe tool dynamometer was used to measure the cutting forces and measuring cutting tool vibration using Pico Scope 2202
Table 1
Chemical composition of IS: 2062, Gr B mild steel
In the present experimental study, spindle speed, feed and depth of cut have been considered as machining parameters The machining parameters with their units and their levels as considered for experimentation are listed in Table 2
Table 2
Machining parameters and their limits
Symbol Machining Parameter Unit Level 1 Level 2 Level 3
Trang 3Table 3
Experimental conditions, cutting force and calculated power
N (RPM)
Feed rate
F (mm/rev)
Depth of cut
d cut (mm)
Response main force
Fc (N)
Cutting speed V c (m min −1 )
Power calculated P c (W = N * V c ) Watt
Table 4
Experimental design and collected response data
Exp
No.
Spindle Speed
N(RPM)
Feed rate f(mm/rev)
Depth of cut
d cut (mm)
Power consumption P(W)
Surface roughness
R a (µm)
Frequency of tool vibration
f (Hz)
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3 Methodologies
3.1 Grey relational analysis
Original Taguchi method has been designed to optimize a single performance characteristic The Grey relational analysis based on the Grey system theory can be used to solve complicated multiple performance parameters effectively As a result, optimization of the complicated outputs can be converted into optimization of a single Grey relational grade Grey relation analysis is used to find out whether there is consistency between the changing trends of two factors or not, and to find out the possible mathematical relationship among the factors or in the factors themselves
3.1.1 Data preprocessing
Data preprocessing is normally required since the range and unit in one data sequence may differ from the others Data preprocessing is also necessary when the sequence scatter range is too large or when the directions of the target in the sequences are different Data preprocessing is a means of transferring the original sequence to a comparable sequence Depending on the characteristics of a data sequence, there are various methodologies of data preprocessing available for the gray relational analysis
If the target value of the original sequence is infinite, then it has a characteristic of the “higher is better.” The original sequence can be normalized as follows:
*
( ) min ( )
( )
max ( ) min ( )
i
x k
−
=
(1)
When the “lower is better” is a characteristic of the original sequence, then the original sequence should be normalized as follows:
*
( )
max ( ) min ( )
i
x k
−
=
(2)
However, if there is a definite target value (desired value) to be achieved, the original sequence will be normalized in from:
*
( ) ( ) 1
max ( ) min
i
x k
−
= −
(3)
Alternatively, the original sequence can be simply normalized by the most basic methodology, i.e., let the value of the original sequence be divided by the first value of the sequence:
0
*
0
( )
(1)
i
i
i
x k
x k
x
where i=1,….,m; k =1,…, n m is the number of experimental data items, and n is the number of parameters
xio(k)denotes the original sequence, xi*(k) the sequence after the data preprocessing, max xio(k) the largest value of xio(k), min xio(k) the smallest value of xio(k), and xio is the desired value of xio(k)
3.2.2 Gray relational coefficient and gray relational grade
In gray relational analysis, the measure of the relevancy between two systems or two sequences is
sequence, and all other sequences serve as comparison sequences called a local gray relation
Trang 5measurement After data preprocessing is carried out, the gray relation coefficient ξ i (k) for the kth
adjusted based on the practical needs of the system) A value of is the smaller, and the distinguished ability is the larger The purpose of defining this coefficient is to show the relational degree between
grey relational coefficient is derived, it is usual to take the average value of the grey relational coefficients as the grey relational grade The grey relational grade is defined as follows:
However, in a real engineering system, the relative importance of various factors varies In the real
was extended and defined as recommended by Deng (1982)
the comparability sequence If the two sequences are identical by coincidence, then the value of grey relational grade is equal to 1
The grey relational grade also indicates the degree of influence that the comparability sequence could exert over the reference sequence Therefore, if a particular comparability sequence is more important than the other comparability sequences to the reference sequence, then the grey relational grade for that comparability sequence and reference sequence will be higher than other grey relational grade Grey relational analysis is actually a measurement of absolute value of data difference between sequences, and it could be used to measure approximation correlation between sequences
4.1 Optimal parameter combination
We know from the analysis of machining process that the lower power consumption and surface roughness as well as lower value of frequency of tool vibration provides better quality of the machined surface Thus, the data sequences power consumption, surface roughness and frequency of tool
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Table 5
Grey relational generation of each performance characteristics
The multi- response optimization problem has been transformed into a single equivalent objective
function optimization problem using this approach The higher grey relational grade is said to be close
to the optimal According to performed experiment design, it is clearly observed that experiment no 16
has the highest Grey relation grade Thus, the sixteenth experiment gives the best multi-performance
characteristics of the turning process among the 27 experiments
Table 6
Surface roughness
R a (µm)
Frequency of tool vibration
f (Hz)
Trang 7Table 7
Grey relational coefficients of each performance characteristics for 27 comparability sequences
Table 8
Evaluated grey relational grades for 27 groups
multi-performance characteristics in the turning process, in sequence can be listed as: factor B (Feed rate), A
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(Spindle speed), C (Depth of cut) Factor B (Feed rate) was the most effective factor to the performance This indicates that the turning performance was strongly affected by the feed rate
Table 9
Response of grey relational grade
Grey relational grade
Total mean Grey relational grade = 0.628
Optimum set of parameters are A in first level, B in first level and C in first level respectively
2 7
2 6
2 5
2 4
2 3
2 2
2 1
2 0
1 9
1 8
1 7
1 6
1 5
1 4
1 3
1 2
1 1
1 0 9 8 7 6 5 4 3 2 1
1 0
0 9
0 8
0 7
0 6
0 5
0 4
0 3
E x p t No
S c a t t e r p l o t o f G r e y r e l a t i o n a l g r a d e v s E x p t N o
vibration
4.2 Confirmation Test
After obtaining the optimal level of the machining parameters, the next step is to verify the
relational grade using the optimum level of the `parameter is the total mean of the grey relational grade
is the mean of the grey relational grade at the optimum level and o is the number of machining parameters that significantly affects the multiple performance characteristics
optimum level and o is the number of machining parameters that significantly affects the multiple
machining parameters can then be obtained Table 10 shows the results of the confirmation experiment using the optimal machining parameters The Power consumption P is greatly reduced from 9.65 to 6.63
greatly reduced from 270.7 to 260 Hz It is clearly shown that multiple performance characteristics in turning process are greatly improved through this study
Trang 9Table 10
Results of machining performance using initial and optimal machining parameters
parameters
Optimal machining parameters
Prediction Experiment Setting Level
A 1 B 1 C 2
A 1 B 1 C 1
A 1 B 1 C 1 Power consumption P(W)
Surface roughness
Improvement in grey relational grade = 0.05
Therefore, a comparison of the predicted values of the power consumption, surface roughness and
be fulfilled simultaneously
5 Conclusion
(AISI T – 42) and IS: 2062, Gr B Mild Steel bar as work material to optimize the turning parameters
design of experiments and Grey relational analysis is constructive in optimizing the multi responses
Based on the results of the present study, the following conclusions are drawn:
mm/rev and Depth-of-cut—0.1 mm)
the real requirements
References
Al-Refaie, A., Al-Durgham, L., & Bata, N (2010).Optimal Parameter Design by Regression Technique
and Grey Relational Analysis The World Congress on Engineering, WCE 2010 3
Arumugam, P U., & Malshe, A P., & Batzer, S A (2006) Dry machining of aluminum silicon alloy
using polished CVD diamond-coated cutting tools inserts Surface Coating Technology, 200, 3399–
3403
Chang, C.K., & Lu, H.S (2007) Design optimization of cutting parameters for side milling operations
with multiple performance characteristics International Journal of Advanced Manufacturing
Technology, 32, 18–26
Deng, J (1982) Control problems of grey systems System Control, 5, 288–294
Kopac, J., & Krajnik, P (2007) Robust design of flank milling parameters based on grey-Taguchi
method International Journal of Advanced Manufacturing Technology, 191, 400–403
Lin, C, L., & Lin, J.L., & Ko, T.C (2002) Optimization of the EDM process based on the orthogonal
array with fuzzy logic and grey relational analysis method International Journal of Advanced
Manufacturing Technology, 19, 271–277
Trang 1060
Lin, Z.C., & Ho, C.Y (2003) Analysis and application of grey relation and ANOVA in
chemical-mechanical polishing process parameters International Journal of Advanced Manufacturing
Technology, 21,10–14
Lin, C.L (2004) Use of the Taguchi method and grey relational analysis to optimize turning operations
with multiple performance characteristics Material Manufacturing Process, 19,209–220
Lo, S.P (2002) The application of ANFIS and grey system method in turning tool-failure detection
International Journal of Advanced Manufacturing Technology, 19, 564–572
Tosun, N (2006) Determination of optimum parameters for multiperformance characteristics in
drilling by using grey relational analysis International Journal of Advanced Manufacturing
Technology, 28, 450–455
Tosun, N (2006) Determination of optimum parameters for multi-performance characteristics in
Tzeng, Y.F., & Chen, F.C (2006) Multi-objective process optimization for turning of tool steels
International Journal of Machining and Machinability of Materials, 1(1), 76–93
Wang, Z.L., & Zhu, J, H., & WU (1996) Grey relational analysis of correlation of errors in
measurement Journal Grey System, 8(1), 73–78.